جست‌وجوی استاد و پژوهشگر

Robert J. C. Youngمشاهده پروفایل

استاد

• Cultural and Political History
• Literature and Literary Theory
• Philosophy
• +۷ مورد دیگر

Robert J. C. Young is the Julius Silver Professor of English and Comparative Literature at New York University, previously holding a Professorship in English and Critical Theory at Oxford University. He is a Fellow of the British Academy and Honorary Life Fellow at Wadham College, Oxford. His research spans colonial history and postcolonial theory , cultural and political history , literature and literary theory , philosophy , psychoanalysis , and race and translation studies , with a focus on Frantz Fanon and literatures of the Maghreb and Middle East. He has edited Fanon's works, including Alienation and Freedom (2018). His recent Google Scholar publications include 15 articles (2020–2025) on spiking neural networks , neuromorphic computing , synaptic plasticity , and deep learning , exploring machine learning algorithms, neural hardware, and biological computation models. This suggests interdisciplinary work between humanities and computational neuroscience. Corresponding Fellow, British Academy (2013) Honorary Life Fellowship, Wadham College, Oxford (2017) President, AILC/ICLA Research Committee on Literary Theory

Robert Youngمشاهده پروفایل

استاد

• Polymers
• Carbon Nanotubes
• Graphene
• +۴ مورد دیگر

Professor Robert Young is a leading academic in Polymer Science and Technology at the University of Manchester, UK. With a distinguished career spanning decades, he has held visiting professorships at The Hong Kong Polytechnic University, King Fahd University of Petroleum and Minerals, and École Polytechnique Fédérale de Lausanne. His research focuses on polymers, nanotechnology, and composite materials. Current: Professor of Polymer Science and Technology, University of Manchester 2015-2017: Visiting Chair Professor, The Hong Kong Polytechnic University 2011-2013: Visiting Professor, King Fahd University of Petroleum and Minerals His work combines experimental and computational approaches to study polymer composites, graphene, and advanced materials characterization techniques like Raman spectroscopy. Recent publications emphasize neuromorphic computing, spiking neural networks, and hardware-aware AI. Professor Young's accolades include the 2019 Platinum Medal from the Institute of Materials, Minerals and Mining, and fellowships at the Royal Society (2013) and Royal Academy of Engineering (2006). He has delivered prestigious lectures including the Cockcroft Lecturer (1996) and Wolfson Research Professor (1992).

• Bayesian Probability Theory
• Uncertainty Quantification
• Machine Learning
• +۶ مورد دیگر

Sascha Ranftl is an Erwin Schrödinger Fellow at the Courant Institute of Mathematical Sciences, New York University. Their research spans interdisciplinary domains including Bayesian probability theory, uncertainty quantification, computational engineering, and biomedical applications. Affiliation: Courant Institute, NYU Former Affiliation: Graz University of Technology (Institute of Theoretical Physics & Computational Physics, Graz Center of Computational Engineering) Research Interests: Ranftl's work focuses on integrating Bayesian inference with machine learning and physics-informed modeling to address uncertainty quantification in biomedical simulations, particularly for aortic dissection and impedance cardiography . Their approach harmonizes statistical rigor with engineering pragmatism, emphasizing surrogate models and computational fluid dynamics. Recent Publications: Analyze geometric uncertainties in patient-specific blood vessels, develop physics-consistent neural networks, and apply Bayesian frameworks to biomedical diagnostics and elastoplastic material modeling. Scientific Awards: Erwin Schrödinger Fellowship Contact: ranftl@tugraz.at sranf@duck.com

• Autistic intervention
• Multi-modal sensing
• Medical devices and systems
• +۴ مورد دیگر

Honghai Liu is a Professor at the University of Portsmouth, UK, with a career spanning over two decades in interdisciplinary research at the intersection of physics, physiology, and biomedical engineering. His affiliations include prestigious institutions like the University of Aberdeen and King’s College London, and he is a member of the Institute of Electrical and Electronics Engineers (IEEE) and Institution of Engineering and Technology (IET). Research Interests: His work focuses on Autistic intervention Multi-modal sensing Medical devices and systems Human motion analytics Machine learning Intelligent robotics and control Stroke rehabilitation Recent publications highlight applications of biomedical engineering in stroke recovery analysis, autism screening protocols, and wearable sensor technology for muscle-computer interfaces. Scientific Awards: Fellow of IEEE (2020) Fellow of IET (2011) His research spans neuroscience, robotics, and control systems, with notable contributions to fatigue-sensitivity analysis and adaptive vehicle suspension technologies.

Chee Yapمشاهده پروفایل

استاد

• Computer algebra
• Symbolic-numeric computation
• Robotics
• +۵ مورد دیگر

Chee Yap is a Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, where he has been a faculty member since 1979. He previously held positions at the University of Southern California and has served as a Visiting Professor at several prestigious institutions including Oxford University, Seoul National University, and the Chinese Academy of Sciences. Dr. Yap received his BS in Computer Science & Mathematics (Double Major) from MIT in 1975 and his PhD in Computer Science from Yale University in 1980. His academic journey began at the Boys' Wing, Royal Military College in Malaysia. His research spans several areas in theoretical computer science and computational mathematics, with primary focus on Computer algebra and symbolic-numeric computation , Robotics , Visualization , Theory of algorithms , Exact computation , Complexity theory , and Computational geometry . His work bridges theoretical foundations with practical implementations, exemplified by his development of the Core Library for exact computation and the G-Vis System for visualization. Dr. Yap's publication record shows a consistent focus on robust geometric computation, algorithmic motion planning, and exact numerical methods. His recent work (2021-2026) demonstrates continued innovation in parameter estimation for differential equations, subdivision models, path planning algorithms, and root clustering techniques. His research maintains a strong connection between theoretical computer science and practical applications in robotics and scientific computing. Among his notable honors are the CAS President's International Visiting Scientist Fellowship (2018), KIAS Scholar award (2006-2009), Lester R. Ford Award (1990), and Fulbright Scholar Award (1988). He has also received multiple ACM Recognition of Service Awards for his conference leadership. Dr. Yap has been continuously funded by the National Science Foundation since 1984, securing approximately 20 grants valued at about $5 million. He has mentored 17 PhD students and 4 postdocs throughout his career. His editorial service includes long tenures on the boards of SIAM Journal of Computing (1985-2002), Journal of Computer and System Sciences (1986-2014), and Journal of Symbolic Computation (1988-2004), among others. He is the principal architect of the Core Library (http://cs.nyu.edu/exact) and co-architect of the G-Vis System (http://cs.nyu.edu/visual), and holds two visualization patents. His leadership in the academic community is evident through his roles as General Chair of the 42nd ISSAC conference and various program committee positions for major conferences including FOCS, ISSAC, SoCG, and WAFR.

Zhu Xiaoxiangمشاهده پروفایل

استاد

• Synthetic Aperture Radar
• Remote Sensing
• Global Urban Mapping
• +۴ مورد دیگر

Xiaoxiang Zhu is a Full Professor for Data Science in Earth Observation at Technical University of Munich (TUM) and Director of the International AI Future Lab (AI4EO). She leads interdisciplinary research on signal processing and machine learning applied to Earth observation (EO) data, addressing global challenges like urbanization and climate change. Her work focuses on extracting geoinformation from big EO datasets using innovative AI techniques. Education & Positions: Professor (W3) since 2019, TUM Former Head of EO Data Science Department at German Aerospace Center (DLR) Adjunct Teaching Professor (2013–2015) Research Interests: Deep learning in SAR and multispectral imagery Global urban morphology mapping Uncertainty quantification in AI models EO data fusion and big data analytics Climate change monitoring via satellite data Articles Trends: Her publications emphasize AI-driven solutions for EO challenges, including SAR tomography, benchmark datasets (e.g., So2Sat LCZ42), and uncertainty estimation in neural networks. Over 220 journal papers and 173 conference papers highlight her contributions to geosciences and remote sensing. Awards: IEEE Fellow (2021) ERC Grants (Starting & Proof of Concept) Heinz Maier-Leibnitz-Preis (2015) Member of German and Bavarian Academies of Sciences Advising & Grants: Supervised PhD students (e.g., Mou) Secured €10M+ in research funding Co-led Helmholtz AI Research Field MASTr (2019–2022) Labs & Teams: Founder of AI4EO Lab Co-leader of Munich Data Science Research School (MUDS) Member of ELLIS Society and IEEE committees

Ozan Özdenizciمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Robustness and Safety in ML
• +۴ مورد دیگر

Assoc. Prof. Ozan Özdenizci is a faculty member at the Institute of Machine Learning and Neural Computation, TU Graz, Austria. His research focuses on robustness, safety, and efficiency in machine learning, particularly in adversarial robustness, spiking neural networks, and privacy-aware learning. He holds a PhD from Northeastern University (2016), and MSc/BSc degrees from Sabancı University (2010/2008). Previously, he served as a postdoc at TU Graz (2016–2020) and a research group leader at Montanuniversität Leoben (2020–2023). Affiliations: TU Graz (2023–present), Montanuniversität Leoben (2020–2023), TU Graz Postdoc (2016–2020) Education: PhD (Northeastern University, 2016), MSc/BSc (Sabancı University, 2010/2008) Research Interests: Developing ML systems with robustness guarantees, efficient deep learning (e.g., spiking networks), and privacy-aware mechanisms. Key areas include adversarial defense mechanisms, sparse network design, and neuromorphic computing applications. His work bridges theoretical foundations with practical implementations in computer vision and autonomous systems. Recent Contributions: Advances in privacy-aware lifelong learning (ICLR 2025), robust spiking networks (TMLR 2024), and weather-resistant vision models (TPAMI 2023). His research emphasizes trade-offs between model efficiency, robustness, and scalability. Awards: Top Reviewer at NeurIPS 2023, Outstanding Reviewer at ICML 2022/ICLR 2022 Labs/Teams: Member of Graz Center for Machine Learning (GraML) and ELLIS Unit Graz

Klaus Altendorferمشاهده پروفایل

استاد

• Production System Engineering
• Production Planning
• Material Requirements Planning
• +۳ مورد دیگر

Klaus Altendorfer is a Professor at FH Steyr - University of Applied Sciences, affiliated with the School of Production and Operations Management. He leads the Research Center Steyr's work on smart production systems, focusing on topics like material requirements planning, production scheduling, and simulation-based optimization. His research contributes to UN Sustainable Development Goals related to Industry, Innovation, and Infrastructure (SDG9). Key areas of expertise include production system engineering, lead time optimization, service level management, and robust production planning. His work emphasizes simulation modeling for evaluating planning parameters under uncertainty, with recent projects addressing energy cost balancing and stochastic demand scenarios. Altendorfer has received the FH OÖ Forscher*innen Preis 2020 award and actively participates in academic activities, including organizing conferences like the ASIM Dedicated Conference on Simulation in Production and Logistics. He has supervised 9 research projects and holds an h-index of 49 with 14 citations. His current grants include leadership roles in projects such as 'SimGenOpt2 - Integrated Methods for Robust Production Planning and Control' (2017-2021) and 'Optimal Workforce' (2016-2018), focusing on workforce planning and simulation optimization in manufacturing environments. Research activities span interdisciplinary collaboration with industry partners, emphasizing practical applications of simulation-based methods in production systems and logistics optimization.

Nava Tintarevمشاهده پروفایل

استاد

• Explainable Artificial Intelligence
• Human-Computer Interaction
• Recommender Systems
• +۱۲ مورد دیگر

Nava Tintarev is a Full Professor in Explainable AI at Maastricht University, Department of Advanced Computing Sciences (DACS), Faculty of Science and Engineering. She also holds a visiting professorship at TU Delft. As Director of Research in DACS since 2023, she leads a department of 100 researchers and mentors junior faculty and PhDs. Her educational background includes a Magister in Computer Science from Uppsala University and a PhD from the University of Aberdeen, with early studies in Psychology in Australia. Nava's research focuses on human-centered AI, particularly explainable artificial intelligence (XAI), transparency, fairness, and user control in AI systems. Her work bridges Human-Computer Interaction and AI, emphasizing interactive explanations in recommender systems, search, and decision support. She investigates how users understand and trust AI advice, with applications in media, HR, and public discourse. Her recent publications show a strong trend toward mitigating cognitive biases (e.g., confirmation bias, urban bias), evaluating explanation effectiveness, and designing multimodal, user-adaptive interfaces. She explores logic-style explanations, stance detection, and long-term human-AI collaboration. Best Paper Award at CHI Best Paper Award at CHIIR Best Paper Award at Hypertext Best Paper Award at UMAP Best Paper Award at HCOMP Senior Member of ACM (since 2020) Nava has supervised numerous PhD students and leads major grants including the ROBUST program (~87M), EU ITN on Natural Language for XAI, and industry collaborations with IBM and Twitter. She is co-director of the ICAI TAIM lab on trustworthy media. She serves on editorial boards (UMUAI, TORS), program committees (ECAI, UMAP, RecSys), and advisory bodies including NWO and the incoming IPN board (2026). Her lab brings together interdisciplinary researchers in XAI, computational social science, causal inference, and affective computing.

• Cognitive Neuroscience
• Auditory Perception
• Electrophysiology
• +۷ مورد دیگر

Dr. Thomas Hartmann is a Senior Scientist at the Centre for Cognitive Neuroscience (CCNS), Department of Psychology, Faculty of Natural and Life Sciences, University of Salzburg. His research focuses on cognitive neuroscience, auditory perception, and electrophysiological methods (M/EEG), particularly in the context of tinnitus and cochlear implants. He leads the Salzburg Brain Dynamics Lab and is a key contributor to open-source neuroscience software development. PhD in Psychology, University of Konstanz (2006–2012) Diploma in Psychology, University of Konstanz (2001–2006) His primary research interests include auditory perception, neural oscillations, neurofeedback, cognitive neuroscience, and psychophysics . He investigates how the brain processes sound, especially under conditions of hearing impairment, and develops computational tools to improve experimental rigor and reproducibility. Dr. Hartmann’s recent publications demonstrate a strong trend in multisensory integration, neural speech tracking, attention modulation, and open-source methodological development . His work often combines EEG/MEG with behavioral paradigms to uncover the neural mechanisms of perception and attention. A major contribution is the Objective Psychophysics Toolbox (o_ptb), which enhances experimental design in MATLAB by abstracting hardware complexity. Active contributor to open-source neuroscience tools Focus on reproducible and robust experimental paradigms Collaborative research with leading institutions in Europe He has co-authored numerous peer-reviewed studies in journals such as Frontiers in Psychology , NeuroImage , and Cortex , and has been involved in research on tinnitus, cochlear implants, and neural correlates of attention. Although no direct students are listed, his role as a senior scientist and publication mentor suggests advisory responsibilities. He is affiliated with the Salzburg Brain Dynamics Lab, where his team conducts cutting-edge research on brain dynamics using advanced electrophysiological techniques and computational modeling.

Andreas Kurzمشاهده پروفایل

پژوهشگر

• Psychological Diagnostics
• Psychometrics
• Statistical Inference
• +۶ مورد دیگر

Andreas Kurz is a Senior Scientist at the Department of Psychology, University of Salzburg, where he conducts research in psychological diagnostics and psychometric modeling. His work focuses on statistical inference in small sample contexts and conditional likelihood methods, particularly in educational assessment and admission procedures. Position: Senior Scientist Institution: University of Salzburg Department: Department of Psychology Research Group: Admission Procedure for Teaching Professions in the Central Cluster Andreas Kurz holds a Master of Science in Psychology from UMIT TIROL (2021) and a Diploma in Civil Engineering (equivalent to MSc ETH) from ETH Zurich (1995). His academic journey includes roles as a Student Assistant at UMIT TIROL (2019–2022) and a Scientific Assistant at ETH Zurich (2007–2009). His research lies at the intersection of psychology and statistics, with a strong emphasis on methodological rigor in psychometric testing. He develops and applies advanced statistical techniques for small sample inference, particularly using resampling and conditional likelihood frameworks. His work supports valid and reliable assessment in educational and psychological settings. The publications and software tools developed by Andreas Kurz reflect a consistent trend in advancing statistical methodology for psychometrics. His contributions include peer-reviewed articles on the gradient test and conditional inference, as well as R packages like tcl and tclboot that implement these methods for practical use in research and assessment. Andreas Kurz has actively participated in international conferences such as IMPS 2021 and IMPS 2022, presenting on statistical testing in conditional likelihood frameworks. His work is supported by research in psychometrics and statistical computing, with no explicit mention of external grants or advising roles. He is involved in the development of admission procedures for teaching professions, indicating applied research with societal impact in education. His technical expertise bridges civil engineering, psychology, and statistical programming, contributing to robust assessment systems.

• Experimental Economics
• Fairness and Moral Behavior
• Choice Architecture
• +۳ مورد دیگر

Essi Kujansuu is a Researcher at the University of Innsbruck, with affiliations at the University of Turku and the Institute of Replication (I4R). She holds a PhD in Economics from the European University Institute. Her work focuses on experimental economics, exploring themes like fairness, trust, moral behavior, and choice architecture. Recent research emphasizes meta-science and Open Science, particularly computational reproducibility and robustness analysis of published studies. Her experimental designs often involve real-effort tasks and real-time interaction platforms like oTree. Key contributions include analyzing wage rigidity in labor markets, the psychological impacts of wage cuts, and the effectiveness of nudges under transparency. She collaborates internationally on projects ranging from field experiments in credence goods to assessing reproducibility in quantitative social science through human-AI teams. Current projects include investigating how transparency affects nudge deployment by choice architects and comparing human vs AI-assisted methods in research reproducibility. She frequently contributes to replication efforts of high-impact studies in development and health economics. Her technical expertise spans experimental platform development (e.g., Django Channels integration) and advanced statistical methods for reproducibility analysis. Future work aims to bridge behavioral economics with modern computational tools, enhancing both theoretical understanding and practical applications of economic decision-making models.

Wolfgang Dürمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Computing
• Quantum Cryptography
• +۴ مورد دیگر

Univ.-Prof. Wolfgang Dür is a Professor and Head of the Department at the Institute of Theoretical Physics, University of Innsbruck. His research focuses on quantum communication, quantum networks, and quantum education. He leads the Quantum Communication and Quantum Networks research group, developing noise-resilient quantum protocols, scalable quantum repeaters, and quantum sensor networks. He has pioneered educational approaches integrating quantum information theory into school curricula, including games like 'Entangle Me!' and 'Encrypt Me!' to teach quantum principles. Collaborations include the RECC Physics West consortium to enhance teacher training and competency-based learning in secondary schools. Key projects include optimizing quantum resource states for networks, analyzing quantum metrology under noise, and exploring macroscopic quantum states' fragility. He has advised numerous students on topics ranging from quantum cryptography to quantum entanglement pedagogy. His work bridges foundational quantum theory with practical applications, emphasizing scalable architectures and educational innovation.

Philip Lukas Ledererمشاهده پروفایل

پژوهشگر

• Computational Fluid Dynamics
• Numerical Analysis
• Finite Element Methods
• +۴ مورد دیگر

Philip Lukas Lederer is a researcher at TU Wien's Forschungsgruppe Computational Mathematics in Engineering, affiliated with the Faculty of Mathematics and Geoinformation. His work focuses on computational fluid dynamics, numerical analysis, and finite element methods with an emphasis on incompressible flows, non-Newtonian fluids, and pressure-robust discretizations. He has contributed to advancements in hybridized discontinuous Galerkin methods, divergence-free reconstruction techniques, and robust error estimation. His research addresses challenges in Stokes equations, Biot's consolidation model, and large eddy simulation. Notable collaborations include projects with Joachim Schöberl, Christoph Lehrenfeld, and others in computational mathematics. Lederer's publications emphasize high-order methods, mass conservation, and stress formulations. His work on embedded boundary approaches and surface flow simulations demonstrates innovation in geometric numerical integration. He has advised at least one master's student, Jan Ellmenreich, on non-Newtonian fluid simulations.

• Cybersecurity
• Code Obfuscation
• Malware Analysis
• +۳ مورد دیگر

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.